# DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning

arXiv: https://arxiv.org/abs/2309.10965 · HTML: https://mlsanity.com/paper/2309.10965/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [DP-CGAN: Differentially Private Synthetic Data and Label Generation](https://mlsanity.com/paper/2001.09700/) (2020)
- [Deep Learning with Gaussian Differential Privacy](https://mlsanity.com/paper/1911.11607/) (2019)
- [Finite Sample Differentially Private Confidence Intervals](https://mlsanity.com/paper/1711.03908/) (2017)
- [Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo](https://mlsanity.com/paper/1502.07645/) (2015)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models](https://mlsanity.com/paper/2103.07101/) (2021)
- [Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing](https://mlsanity.com/paper/1602.03090/) (2016)
- [Differentially Private Quantiles](https://mlsanity.com/paper/2102.08244/) (2021)
- [Bayesian Differential Privacy through Posterior Sampling](https://mlsanity.com/paper/1306.1066/) (2013)
- [Locally Private Mean Estimation: Z-test and Tight Confidence Intervals](https://mlsanity.com/paper/1810.08054/) (2018)
- [Comparative Study of Differentially Private Data Synthesis Methods](https://mlsanity.com/paper/1602.01063/) (2016)
- [Differentially private significance tests for regression coefficients](https://mlsanity.com/paper/1705.09561/) (2017)
- [Differentially Private Nonparametric Hypothesis Testing](https://mlsanity.com/paper/1903.09364/) (2019)
- [Differentially Private Confidence Intervals for Empirical Risk Minimization](https://mlsanity.com/paper/1804.03794/) (2018)
- [Pain-Free Random Differential Privacy with Sensitivity Sampling](https://mlsanity.com/paper/1706.02562/) (2017)
- [Comparing Population Means under Local Differential Privacy: with Significance and Power](https://mlsanity.com/paper/1803.09027/) (2018)
- [Statistical Properties of Sanitized Results from Differentially Private Laplace Mechanism with Univariate Bounding Constraints](https://mlsanity.com/paper/1607.08554/) (2016)
- [A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning](https://mlsanity.com/paper/2307.13127/) (2023)

## Abstract (arXiv)

Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for individuals when releasing aggregated statistics or building statistical/machine learning models from data. We develop the open-source R package DPpack that provides a large toolkit of differentially private analysis. The current version of DPpack implements three popular mechanisms for ensuring DP: Laplace, Gaussian, and exponential. Beyond that, DPpack provides a large toolkit of easily accessible privacy-preserving descriptive statistics functions. These include mean, variance, covariance, and quantiles, as well as histograms and contingency tables. Finally, DPpack provides user-friendly implementation of privacy-preserving versions of logistic regression, SVM, and linear regression, as well as differentially private hyperparameter tuning for each of these models. This extensive collection of implemented differentially private statistics and models permits hassle-free utilization of differential privacy principles in commonly performed statistical analysis. We plan to continue developing DPpack and make it more comprehensive by including more differentially private machine learning techniques, statistical modeling and inference in the future.